# Rule-based Emotion Detection on Social Media: Putting Tweets on Plutchik's Wheel

arXiv: https://arxiv.org/abs/1412.4682 · HTML: https://mlsanity.com/paper/1412.4682/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Dec 2014.

## Built on by (most cited first, 1 of 1)

- [Emotion-aware Dual Cross-Attentive Neural Network with Label Fusion for Stance Detection in Misinformative Social Media Content](https://mlsanity.com/paper/2505.23812/) (2025)

## Abstract (arXiv)

We study sentiment analysis beyond the typical granularity of polarity and instead use Plutchik's wheel of emotions model. We introduce RBEM-Emo as an extension to the Rule-Based Emission Model algorithm to deduce such emotions from human-written messages. We evaluate our approach on two different datasets and compare its performance with the current state-of-the-art techniques for emotion detection, including a recursive auto-encoder. The results of the experimental study suggest that RBEM-Emo is a promising approach advancing the current state-of-the-art in emotion detection.